Architecture for an Adaptive and Intelligent Tutoring System that Considers the Learner's Multiple Intelligences

The majority of adaptive and intelligent tutoring systems (AITS) are dedicated to a specific domain, allowing them to offer accurate models of the domain and the learner. The analysis produced from traces left by the users is didactically very precise and specific to the domain in question. It allows one to guide the learner in case of difficulty and to offer her/him some support. This paper's objective was to develop an (AITS), adapted for letting the learners work in several disciplinary fields of the University of the Annaba. In this context, its constraint is threefold: to represent knowledge relative to several disciplinary domains, to propose interactive activities to the learners based on multiple intelligences, and finally, to be able to support student guidance in her/his course by proposing her/him relevant support activities when she/he meets difficulties. The proposed system covers all important properties such as hypertext component, adaptive sequencing, problem-solving support, intelligent solution analysis and adaptive presentation while available systems have only some of them.

Agent-based adaptive e-learning model for any learning management system

Many scholars are interested in improving e-learning in order to provide easy access to educational materials. There is, however, the need to incorporate the ability to classify learners into these learning systems. Learner classification is used adaptively to provide relevant information for the various categories of learners. There is also a need for learning to continue, whether learners are on- or off-line. In many parts of the world, especially in the developing world, most people do not have reliable continuous internet connections. We tested an Adaptive e-Learning Model prototype that implements an adaptive presentation of course content under conditions of intermittent Internet connections. This prototype was tested in February 2011 on undergraduate students studying a database systems course. This study found out that it is possible to have models that can adapt to characteristics such as the learner’s level of knowledge and that it is possible for learners to be able to study under both on- and off-line modes through adaptation.